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Abstract-- This work explored the benefits of increasing

transmission capacity between the US Eastern and Western

Interconnections under a high-renewables future. Given the

existing “seam” between the two interconnections, a co-optimized

infrastructure planning model was developed to assess tradeoffs

between investments in cross-seam HVDC transmission, AC & DC

transmission needs within each interconnection, generation

investment costs, and operational costs, at different renewable

penetration levels. The model allows existing and candidate

generation resources for deliverable planning reserves to be

shared throughout the interconnections, a feature which drives

identification of least-cost investments. This work is performed

using an industry-vetted 169-bus representation of the North

American power grid. Results from this analysis indicate that,

under high wind/solar growth scenarios, the cost of a macrogrid

HVDC transmission is outweighed by the generation-related

savings they produce. The presence of other benefits related to

grid reliability, resilience, and adaptability, suggest that cross-

seam transmission is highly attractive infrastructure.

Index Terms- HVDC, transmission/generation planning

I. NOMENCLATURE

INDEXES 𝑦/𝑁𝑦 Year/Planning horizon

𝑔/𝑁𝑔 Area/Total number of areas

𝑠/𝑁𝑠 Block/Total number of blocks ℎ/𝑁ℎ Single/Total number of generation technologies 𝑡/𝑁𝑡 Single/Total number of transmission technologies

SETS 𝐸 Set of energy blocks 𝑃 Set of non-coincident peak-load blocks 𝐺 Set of reserve sharing groups (RSG) 𝑀 Set of HVDC transmission lines

DECISION VARIABLES 𝑃 Generation dispatch 𝐶𝑁𝑒𝑤 New generation capacity 𝐶𝑅𝑒𝑡 Retired generation capacity 𝐶 Cumulative generation capacity 𝑇𝐶𝑁𝑒𝑤 New transmission capacity 𝑇𝐶 Cumulative transmission capacity 𝑅𝑈 Regulation reserve up

This work was supported in part by the U.S. Department of Energy, the

National Science Foundation (Award 1069283), and the ISU Electric Power Research Center.

A. L. Figueroa-Acevedo was with Iowa State University and is now with Policy Studies at the Midcontinent Independent System Operator, Eagan, MN USA (e-mail: afigueroa-acevedo@misoenergy.org).

J. McCalley, A. Jahanbani-Ardakani, and A. Venkatraman are with the Electrical and Computer Engineering Department, Iowa State University, Ames, IA USA (email: jdm@iastate.edu, alij@iastate.edu, vabhinav@iastate.edu).

H. Nosair was with Iowa State University and is now with the New York Independent System Operator, Rensselaer, NY 12144 USA (email: hnosair@nyiso.com).

𝑅𝐷 Regulation reserve down 𝐶𝑅 Contingency reserve 𝜃𝑖 Voltage phase angle (sending) 𝜃𝑖 Voltage phase angle (receiving) 𝑓 Branch flow

PARAMETERS 𝐶𝐴𝑃𝐸𝑋 Generation investment cost in $/MW 𝐼𝐶 Transmission investment cost in $/MW 𝐹𝑂𝑀 Fixed operation and maintenance cost in $/MW-yr 𝑉𝑂𝑀 Variable operation and maintenance cost in $/MWh 𝐻𝑅 Weighted-average heat rate in MBTU/MWh 𝐹𝐶 Fuel cost in $/MBTU ∆𝑠 Block duration in hours 휀 Discount factor 𝑟 Real discount rate 𝐷 Demand in MW 𝐶𝐸𝑥𝑖𝑠𝑡 Existing generation capacity in MW 𝐶𝐹 Capacity factor 𝐶𝑉 Capacity value using deterministic approach 𝛼 Coefficient to scale regulation up requirements 𝛽 Coefficient to scale regulation down requirements 𝛿 Largest credible contingency 𝑟𝑟1−𝑚𝑖𝑛 Ramp rate in MW/min 𝑋 Reactance in per unit (100 MVA base) 𝑇𝑀𝑎𝑥 Transmission investment cap 𝐹𝑂𝑅 Forced outage rate 𝐷𝐸𝐶 Decommissioning cost of generation in $/MW 𝜋𝑟𝑢 Regulation up reserve scaling factor in $/MWh 𝜋𝑟𝑑 Regulation down reserve scaling factor in $/MWh 𝜋𝑐𝑟 Contingency reserve scaling factor in $/MWh

II. INTRODUCTION

HE Federal Energy Regulatory Commission (FERC)

defines “interregional transmission facility” in its Order

1000 [1] as a transmission facility that is physically located in

two or more transmission planning regions. FERC currently

identifies 14 planning regions that cover most of the continental

US. The coordination of markets through Regional

Transmission Operators (RTOs), motivated by FERC Order

1000, has been central in identifying interregional transmission

investment opportunities with high economic, reliability, and

policy value. In addition, there are independent merchant

transmission developers who have proposed interregional

Aaron Bloom was with the National Renewable Energy Laboratory and is now with the Product R&D at NextEra Analytics, St. Paul, MN (Aaron.bloom@nexteraanalytics.com).

D. Osborn is retired from the Midcontinent ISO, Eagan, MN (email: macrogrid@outlook.com).

Jay Caspary and Harvey Scribner are with the Research, Development & Tariff Services at the Southwest Power Pool, Little Rock, AR USA (email: JCaspary@spp.org; HScribner@spp.org).

James Okullo and Jordan Bakke are with Policy Studies at the Midcontinent Independent System Operator, Eagan, MN USA (email: jokullo@misoenergy.org; jbakke@misoenergy.org).

Design and Valuation of High-Capacity HVDC

Macrogrid Transmission for the Continental US Armando L. Figueroa-Acevedo, Member, IEEE, Ali Jahanbani-Ardakani, Hussam Nosair, Abhinav

Venkatraman, James D. McCalley, Fellow, IEEE, Aaron Bloom, Dale Osborn, Sr. Member, IEEE,

Jay Caspary, Member, IEEE, James Okullo, Jordan Bakke, and Harvey Scribner, Member, IEEE

T

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transmission projects [2]-[7]. Most interregional projects

considered to date have been internal to either the Eastern

Interconnection (EI) or to the Western Interconnection (WI). In

this work, we consider interregional transmission expansion

between the EI and WI.

In 1967, the EI and WI were interconnected with 230 kV AC

lines in Montana, the Dakotas, and Nebraska, but separations

occurred frequently, and the connections were permanently

opened in the 1970’s [8]. Although [9] and [10] reported studies

of promising cross-seam AC interconnections, subsequent

interconnections between the EI and WI have been limited to

high-voltage direct-current (HVDC) back-to-back (B2B)

connections. Recent studies have suggested that increasing

transmission capacity between the EI and WI creates significant

economic value related to renewable energy integration [11]-

[13]. The work reported in this paper quantifies benefits

associated with high capacity HVDC cross-seam transmission

expansion, establishing such an investment as being a clearly

attractive grid development for reducing cost while increasing

the long-term integrity of the electric infrastructure. This paper

is organized as follows. Section III identifies the drivers of

interregional transmission development. In Section IV the co-

optimization model developed in this work is described. Section

V presents the assumptions and the design process adopted. The

major findings related to the benefits of increasing transmission

capacity between the EI and WI are presented in Section VI,

including results of a sensitivity assessment under future

uncertainties. Section VII concludes.

III. DRIVERS OF INTERREGIONAL TRANSMISSION

The benefits of interregional transmission include access of

load centers to higher quality renewable resources,

interregional sharing of the most economic energy resources,

increased interregional sharing of operational and planning

reserves, and increased interconnectedness and deliverability

with consequential effects on reliability, resilience, and

adaptability. These are described in what follows.

A. Resource quality

Access to high-quality renewables reduces the levelized cost

of electricity and facilitates the implementation of cost-

effective renewable energy policies. This is reflected in the

most recent RTO generation interconnection queues. For

example, on the EI side, the Midcontinent Independent System

Operator (MISO) interconnection queue includes 42 GW of

wind projects and 38 GW of solar projects [14]. Likewise, wind

and solar projects in the Southwest Power Pool (SPP)

generation interconnection queue account for nearly 100% of

the requests [15]. On the WI side, the California Independent

System Operator (CAISO) current interconnection queue

includes 18 GW of wind and solar projects [16]. This trend is

expected to continue as technological advances in wind and

solar resources evolve and their economics become

increasingly attractive.

B. Interregional sharing on a diurnal basis of the most

economic energy resources

Daily diversity in peaking time refers to the extent to which

different regions peak at different times over each 24 hour

period (largely due to time zone differences), thus enabling

increased use of the most efficient units between regions and

particularly across interconnections. Additional diurnal benefits

of interregional transmission include expanded geographical

coverage for frequency regulation and therefore reduced

variability in solar and wind due to geo-smoothing.

Furthermore, increased duration of access to solar resources,

due to the integration between east and west coast systems,

reduces Eastern and Central US end-of-day ramping

requirements relative to what these regions see in isolation. The

daily benefits also include primary frequency response sharing,

resulting in improved reliability performance and cost reduction

if such markets are developed.

C. Interregional sharing of peaking capacity

Weather patterns and time-zone differences are among the

principal sources of annual peak load diversity in continent-

wide interconnections [17]. When the peak load time of two or

more regions differs, and the spare capacity of one region

relative to one or more other regions is large enough, increasing

transmission capacity between them creates value by allowing

two or more balancing authorities (BAs) to maximize the use of

local generation resources during the annual peak load

conditions of each respective BA. In [18], the minimum load

diversity between the US EI and WI was estimated to be 30 GW

when neglecting transmission constraints, based on 7 years of

historical load data. With the addition of frequency response

sharing, energy arbitrage and wind diversity, the resulting

benefit-to-cost ratio of a conceptual HVDC macro-grid overlay

was 1.25. A different study [19] used two years of synchronized

hourly load data and showed that load diversity creates savings

when combined with other value drivers (e.g. renewable energy

and operating reserves sharing). However, additional value

drivers such as the sharing of frequency response obligations

[20], integration of renewables [21], and sharing of regulation

and other intra-hour reserves (e.g., load following) [22],

increases the economic and reliability value of a well-designed

interregional HVDC transmission infrastructure.

D. HVDC technological capabilities

Although the majority of interregional transmission projects

across the US remain AC, HVDC technology has been the

preferred option worldwide when considering interconnections

between asynchronous systems [23]-[25]. Its controllability and

black-start capabilities reduce the risk of cascading outages and

facilitate the sharing of resources in continent-wide

interconnections. Additionally, HVDC offers the ability to

hedge against financial risks (e.g., reduction in market price

volatility), extreme weather events and fuel unavailability risks

(e.g., Polar Vortex, Hurricane Katrina). Furthermore, the

power transfer capability of HVDC is independent of distance,

power can be scheduled from source-to-sink without causing

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loop flow in the underlying AC system, and power angle

limitations can be mitigated [18]. Finally, the HVDC

technology offers long-distance transmission at less cost than

AC solutions while offering unique ways to provide additional

voltage and frequency control capabilities.

E. Increases interconnectedness and deliverability

Deliverability between two regions is considered here to be

the ability of the system to deliver generation in one region to

the load of the other region while maintaining adequate levels

of reliability. Although previous research has investigated the

benefits of increasing deliverability for renewable integration in

the US, most have focused on either the EI side or the WI side

[26], [27]. Previous work on interregional transmission at the

national level is limited, and the application of co-optimization

techniques has been applied only in a few studies. A

transportation model for transmission was used in [21] to design

an HVDC infrastructure for the continental US. Results from

this work showed that high capacity HVDC transmission

expansion between the EI and WI is attractive. The National

Renewable Energy Laboratory (NREL) Renewable Electricity

Futures study (RE Futures) [28] showed that approximately 35

GW of transmission expansion between the EI and WI could

potentially facilitate 80% renewable generation in the US when

electrification in the transportation infrastructure is considered,

and it was found in the work of [12] that 42 GW of cross-seam

transmission is an optimal value under a high inland wind

scenario. The work reported in this paper further contributes to

the literature by explicitly accounting for capacity sharing

between the EI and WI in a co-optimization framework. From

a long-term capacity expansion planning modeling perspective,

this work extends that of [12] and [18] through improved data,

more rigorous modeling, and expanded sensitivity work, all

completed under the oversight of a project advisory board

comprised of representatives from more than 30 utilities and

organizations within the energy industry [29].

IV. CO-OPTIMIZED EXPANSION PLANNING MODEL

The problem is formulated as a co-optimized expansion

planning (CEP) linear programming model (LP), where the

NPV of two different yet interdependent infrastructures (e.g.,

transmission and generation) are simultaneously minimized

within one optimization formulation as shown in (1)-(21). We

describe this model in what follows.

Min CAPEX(y, g, h)𝐶𝑦𝑔ℎ𝑁𝑒𝑤+FOM(g, h)𝐶𝑦𝑔ℎ

𝑇𝑜𝑡 +𝑇𝐶 (t, k)𝑇𝑦𝑡𝑁𝑒𝑤

+𝑃𝑦𝑔ℎ𝑠[𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙ 𝐻𝑅(ℎ) + 𝑉𝑂𝑀(ℎ)] + 𝜋𝑟𝑢 ∙ 𝑅𝑈𝑦𝑔ℎ𝑠 ∙[𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠)𝐻𝑅(ℎ)] + 𝜋𝑟𝑑 ∙ 𝑅𝐷𝑦𝑔ℎ𝑠 ∙ [𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙𝐻𝑅(ℎ)] + 𝜋𝑐𝑟 ∙ 𝐶𝑅𝑦𝑔ℎ𝑠 ∙ [𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙ 𝐻𝑅(ℎ)] +(𝑉𝑂𝐿𝐿)𝑈𝐸𝑦𝑘 + DEC(y, g, h)𝐶𝑦𝑔ℎ

𝑅𝑒𝑡 (1)

Subject to:

∑ ∑ 𝑃𝑦𝑔ℎ𝑠𝑁ℎℎ=1

𝑁𝑔𝑔=1 ± ∑ 𝑓𝑦𝑡

𝑁𝑡𝑡=1 + 𝑈𝐸𝑦𝑠 = 𝐷(𝑦, 𝑔, 𝑠) ∀𝑠 ∈ 𝐸 (2)

∑ 𝑃𝑦𝑔ℎ𝑠𝑁ℎℎ=1 ± ∑ 𝑓𝑦𝑡

𝑁𝑡𝑡=1 + 𝑈𝐸𝑦𝑘 = 𝐷(𝑦, 𝐺, 𝑠) × (1 +

𝑃𝑅𝑀) ∀𝑠 ∈ 𝑃 (3) 𝐶𝑦𝑔ℎ

𝑇𝑜𝑡 = 𝐶𝑦𝑔ℎ0 + 𝐶𝑦𝑔ℎ

𝑁𝑒𝑤 − 𝐶𝑦𝑔ℎ𝑅𝑒𝑡 (4)

𝑃𝑦𝑔ℎ𝑠 + 𝑅𝑈𝑦𝑔ℎ𝑠 + 𝐶𝑅𝑦𝑔ℎ𝑠 ≤ 𝐶𝑦𝑔ℎT𝑜𝑡 ∀ 𝑠 ∈ 𝐸 (5)

𝑃𝑦𝑔ℎ𝑠 ≤ 𝐶𝑉(𝐺, ℎ) × 𝐶𝑦𝑔ℎ𝑇𝑜𝑡 ∀ 𝑠 ∈ 𝑃 (6)

𝑃𝑦𝑔ℎ𝑠 − 𝑅𝐷𝑦𝑔ℎ𝑠 ≥ 𝑃𝑚𝑖𝑛(𝑔, ℎ, 𝑠) ∀ 𝑠 ∈ 𝐸 (7)

∑ ∑ 𝑅𝑈𝑦𝑠𝑔ℎ𝑁ℎℎ

𝐺𝑔=1 ≥ 𝛼 ∗ 𝜎𝑁𝐿−𝑢𝑝

1𝑚𝑖𝑛 ∀ 𝑔 ∈ 𝑁𝑔 (8)

∑ ∑ 𝑅𝐷𝑦𝑠𝑔ℎ𝑁ℎℎ

𝐺𝑔=1 ≥ 𝛽 ∗ 𝜎𝑁𝐿−𝑑𝑜𝑤𝑛

1𝑚𝑖𝑛 ∀ 𝑔 ∈ 𝑁𝑔 (9)

∑ ∑ 𝐶𝑅𝑦𝑠𝑔ℎ𝑁ℎℎ

𝐺𝑔=1 ≥ 𝛿 ∗ 𝐷(𝑦, 𝑔, 𝑠) ∀ 𝑔 ∈ 𝑁𝑔 (10)

𝑅𝑈𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ1𝑚𝑖𝑛𝐶𝑦𝑔ℎ (11)

𝑅𝐷𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ1𝑚𝑖𝑛𝐶𝑦𝑔ℎ (12)

𝐶𝑅𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ10𝑚𝑖𝑛𝐶𝑦𝑔ℎ (13)

𝜃𝑦𝑠𝑔𝑖 − 𝜃𝑦𝑠𝑔𝑗 = 𝑋𝑡(𝐵𝑦𝑠𝑡) (14)

𝑇𝑦𝑘𝑡 = 𝑇𝑦𝑘𝑡0 + ∑ 𝑇𝑦𝑘𝑡

𝑁𝑒𝑤𝑁𝑘𝑘=1 (15)

−𝑇𝑦𝑘𝑡 ≤ 𝐵𝑦𝑠𝑘𝑡 ≤ 𝑇𝑦𝑘𝑡 (16)

𝑇𝑦𝑘𝑡𝑁𝑒𝑤 ≤ 𝑇𝑦𝑘𝑡

𝑀𝑎𝑥 (17)

𝑇𝑦𝑘1𝑡 = 𝑇𝑦𝑘2𝑡 = . . . 𝑇𝑦𝑘𝑀𝑡 (18)

𝐶𝑦𝑔ℎ𝑁𝑒𝑤 ≤ 𝐶𝑦𝑔𝑡

𝑀𝑎𝑥 (19)

𝐶𝑦𝑔ℎ𝑁𝑒𝑤, 𝐶𝑦𝑔ℎ

𝑅𝑒𝑡 , 𝑃𝑦𝑔ℎ𝑠 , 𝑇𝑦𝑘𝑡𝑁𝑒𝑤 , 𝑅𝑈𝑦𝑔ℎ𝑠 , 𝑅𝐷𝑦𝑔ℎ𝑠 , 𝐶𝑅𝑦𝑔ℎ𝑠 ≥ 0 (20)

The objective (1) of our least-cost co-optimized generation

and transmission model (CGT-Plan) is to minimize the net-

present value (NPV) of generation investments, transmission

investments, generation retirements, the production cost of

generation resources, the cost of providing regulation up,

regulation down, and contingency reserves, the operations and

maintenance costs of new and existing generation resources,

and the value of loss of load. The objective function is refined

to account for technology maturation rates and regional cost

multipliers as presented in [30]. The capital expenditure

(CAPEX) is used for new generation resources to represent the

total cost required to achieve commercial operation. The

retirement decision is driven by the capacity factor and FOM:

the model retires a generation technology in any year where it

does not provide operating reserves, planning reserves or

energy, an indication that its FOM exceeds its economic benefit.

The CEP formulation addresses end-effects by including 20

years of operation beyond the planning horizon. This accounts

for life-time operational costs of technologies invested in the

later years of the planning horizon. A discount factor is applied

to each term in the objective function to account for the time

value of money.

The objective function is evaluated subject to nodal power

balance constraints for the energy blocks (2), nodal power

balance constraints for the peak-load blocks (3), cumulative

generation capacity (4), generation dispatch upper limits for the

energy blocks (5), generation dispatch upper limits for the peak-

load blocks (6), and generation minimum stable level

constraints (7). Equation (3) guarantees that capacity is allowed

to be exchanged and delivered using existing and new

transmission. We use the term “PRM deliverability” to indicate

the extent to which transmission is available to deliver

generation capacity necessary to meet requirements associated

with planning reserve margins (PRM). To account for the non-

coincident peak loads, historical load data is used to

parameterize the right-hand-side of (3). For each region k, its

peak block is represented by its own peak scaled to account for

its required margin (e.g., 1.15) while the rest of the regions j≠k

are characterized by an expected load level at the time region k

is peaking. The sum over all j≠k of the differences between the

region j peak and the region j expected load level during the

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region k peak is defined as the annual load diversity for region

k. The annual load diversity of any two or more regions was

determined using multiple years of historical load data [18]. The

capacity value (CV) in (6) accounts for the availability of

variable resources during peak load times. The effective load

carrying capability (ELCC) was used to characterize the CV of

peaking regions [34], [35]. The CV of non-peaking (assisting)

regions is computed as the average available power during the

top ten net-load peak times of the peaking month. In this

approach, CVs computed (based on ELCC) are considered to

conservatively represent renewable generation levels for the

peaking region k, while the CVs computed (based on average

of top-ten net-load peak times) are considered to be slightly less

conservative for assisting regions j≠k. This results in a balance

of conservative need in the peaking region with credible ability

in the assisting regions. In order to account for the effects of

renewables on the short-term operation of a power system, a set

of operating reserve constraints are defined as a function of net-

load variability. Equations (8)-(13) show the regulation up,

down, and contingency reserve requirements. The capability of

a generation technology to ramp up/down in providing

regulation reserves is modeled as a function of the net-load

variability, represented by the standard deviation σ of 1-minute

changes in net load. Coefficients α and β are parameters used to

scale σ; they are estimated using hourly wind, solar and load

delta profiles, such that the 99th percentile of deltas comply with

NERC’s CPS-2 standard. The implication of (8) and (9) is that

as wind and solar investments increase, so do the net-load

variability. Regulation reserve requirements increase as a

function of the net-load variability and are only provided by

thermal and hydro technologies. Coefficient δ in (10) represents

the largest credible contingency as a percentage of the demand.

The rest of the constraints include the DC power flow and

thermal limit constraints for existing and new transmission

(14)-(17). Equation (18) is included to address the need for the

design to be self-contingent; it requires all HVDC candidate

segments have equal capacities. For a given total transfer level,

requiring equal capacities minimizes pre-contingency

curtailment to satisfy N-1 contingency criteria, consistent with

the rule of three [11]. Equation (19) is included to limit the

generation investments as a function of population density, land

availability, and resource potential. Finally, (20) defines all

decision variables to be non-negative. This mathematical

formulation was implemented using GAMS and solved using

CPLEX. The code was validated by comparing its results to

that of similar codes obtained from developers at other

institutions. The results of these efforts were favorable and can

be found in [30].

V. DESIGN PROCESS

The formulation presented in the previous section was

implemented using a model of the North American electric

power grid, as part of the Interconnection Seam Study [29].

This section describes the assumptions underlying the base

conditions used in the study.

Hourly generation, hydro, and load data corresponding to

year 2024 were obtained from NREL, based on [36] and [37].

The existing generators are energy-limited by their capacity

factor and capacity-limited by their forced outage rate (FOR).

The investment and operational cost data for new generation

resources were gathered from the 2017 annual technology

baseline (ATB) [31]. Maturation rates for candidate generation

technologies were gathered from NRELs 2017 annual

technology baseline [31] and the regional multipliers from [32].

The transmission regional multipliers were adapted from [33].

An additional cost was included in the CAPEX of candidate

wind and solar generation technologies to account for the

transmission necessary to connect resources located in remote

locations. The cost of each transmission spur line was

calculated based on the distance to the closest bus, kV level,

terrain type, and base cost data, gathered from [13] and [38].

For the WI side, Kron reduction [39] was used to create a

reduced network equivalent of a 2026 power flow case obtained

from the Transmission Expansion Planning Policy Committee

(TEPPC) of the Western Electricity Coordinating Council

(WECC). A total of 101 buses, including 7 buses for modeling

existing back-to-back (B2B) DC ties to the EI, were selected

and preserved, retaining most key paths in the WECC region as

defined in the notes for the TEPPC 2026 power flow case [40].

Kron reduction distributes current injections of an eliminated

bus to the remaining (preserved) buses; we refer to the factors

computed for this purpose as the fractional mapping of the

eliminated bus. The fractional mapping was used to relocate

load of eliminated buses in fractions, whereas the highest

fraction was selected to relocate generation of eliminated buses

integrally so as to retain the identity of individual generation

units. The EI model, developed by engineers from the

Midcontinent Independent System Operator (MISO), used 61

buses, with 7 buses for modeling existing B2B DC ties to the

WI. MISO used the software Transmission Adequacy and

Reliability Assessment (TARA) [41] to calculate transfer limits

between connected buses under N-1 conditions. The equivalent

impedances for all lines within the EI were estimated based on

knowledge of voltage level connecting each pair of regions,

distance, and transfer limit. Figure 1 shows the 169-bus

representation of the model used in this work.

Interregional transmission candidate lines were assumed to be

overhead, including high-voltage AC and HVDC technologies.

Base cost data of transmission was gathered from [13] and [38].

The base costs per mile for transmission were escalated from

the 2014 values to 2024 using an inflation rate of 2%. Natural

gas, oil, and coal fuel prices are consistent with forecasted

values in [42]. Based on review of governmental documents

and previous studies, a 5.7%/year real discount rate was

assumed with 2%/year inflation. A 3%/year increase in

distributed generation was adopted.

A. Selection of typical operating blocks

Raw data was obtained in the form of 8760-hour time

series of wind, solar, hydro, and load for a highly granular set

of points characterizing the entire US. In order to capture the

diurnal diversity of wind, solar, hydro and load, all time-series

were referenced to a common time. An operating block is a T-

hour operating condition that represents T similar 1-hour

5

operating conditions; the operating conditions need not be (and

typically are not) sequential. A total of 19 blocks were

developed to represent each year. A 5-block representation of a

typical 24-hour period was used for the production cost model

(e.g. the energy blocks). This approach is an adaptation from

[43]. Each energy block was characterized by the average over

the hours represented by the block of load, wind, solar, and

hydro production levels, and a capacity addition above net-load

to account for operating reserve products (regulation up,

regulation down, and contingency). Three seasons were defined

to capture the annual variation of wind, solar, hydro and load:

winter (November, December, January, February), summer

(May, June, July, August), and shoulder (March, April,

September, October). Finally, four 1-hour duration blocks were

defined to represent the conditions throughout the model

corresponding to the peak load of each of four reserve sharing

groups (RSGs). The four RSGs were defined based on time-

zones. These blocks are characterized by the peak-load of the

RSG that is peaking and an expected load for all others RSGs

that are not peaking as presented in Section IV. A 15% planning

reserve margin above peak was enforced on each peak-load

block.

B. Description of designs and conditions

In order to quantify the benefits of a conceptual HVDC

macrogrid overlay, a co-optimized plan was developed for a

benchmark case called Design 1, where it was assumed the

existing B2B ties remain as currently existing so that the EI-WI

(“cross-seam”) transmission capacity is maintained fixed at

1.31GW. Three other designs were studied:

Designs 2a and 2b both enabled economically optimal

unconstrained growth of transmission capacity at the

existing B2B ties. Whereas this was the only cross-seam

transmission growth allowed in Design 2a, Design 2b also

allowed cross-seam transmission growth via three East-

West HVDC lines, constrained to have equal capacities,

with terminals located at interior points of the two

respective interconnections, and terminal locations chosen

to maximize cross-seam transmission value.

Design 3 allowed HVDC growth only in 15 segments of an

HVDC macrogrid network, with segment capacities

constrained to be equal. This design did not allow

additional growth of transmission at the existing B2B ties.

All designs allowed unconstrained growth of generation and

AC transmission, with two exceptions: (a) generation growth

for the entire model over the 15-year period was capped at 600

GW to recognize regulatory process and construction limits on

what could be physically built in 15 years; (b) AC transmission

expansion could only occur on existing circuits. The planning

horizon was 2024-2038. The four transmission designs were

studied across two different renewable penetration levels: 50%

and 40% (by energy, including wind, solar, and hydro). The

generation mix of the 50% case was obtained without RPS

constraints enforced and with an escalating emissions price of

$3/tonne/year, starting from $3/tonne in 2024 and growing to

$45/tonne in 2038. This approach, referred to as the base

condition, was driven by the study Technical Review

Committee (TRC) as a proxy for potential growth in wind and

solar. The TRC viewed that projections for wind and solar

installations have historically been conservative, and the

inclusion of an emission price is an objective method to

increase the amount of wind and solar on a system in light of

uncertainty in traditional forecasts for deployment. The

generation mix of the 40% case was obtained with constraints

associated with existing (as of 2016) renewable portfolio

standards (RPS) enforced, and no emissions price – we refer to

this case as “current policy.” Results for the base condition and

the current policy are provided for Designs 1 and 3; results for

sensitivities are provided for Design 3; results for Designs 2a

and 2b may be obtained in [19].

VI. ECONOMIC VALUE OF HVDC MACROGRID

INFRASTRUCTURE

The benefits of Design 3 are shown in Table 1, and Fig. 2

illustrates the cumulative transmission and generation additions

for Design 3 under base assumptions. The economic benefits

were calculated as the difference (the “delta”) in the total 15-yr

NPV between Designs 1 and 3 in terms of (1) the following

objective function non-transmission components: generation

investment cost, O&M costs (fuel cost, FOM and VOM cost,

regulation-up and –down reserve cost), emission cost, and

contingency reserve cost; and (2) the line investment cost (AC

and DC). All non-transmission deltas were summed and divided

by the transmission delta to get the B/C ratio.

Table 1: Benefits of increasing transmission capacity between the EI and

WI for 50% renewable case.

Objective Function Design 1 Design 3 Delta

Line Investment (B$) 61.21 80.10 18.89

Generation Investment (B$) 704.03 700.51 -3.52

O&M (B$) 1336.36 1300.70 -35.66

Emission Cost (B$) 171.10 162.50 -8.60

15-yr B/C Ratio - - 2.52

Figure 1: Cumulative transmission, wind and solar addition from 2024 to

2038for the macrogrid design (Design 3) for 50% renewable case

A. Discussion

There are four significant observations to make relative to

this design, as follows:

1. Figure 2 indicates the locations for the highest quality wind

(Midwest) and solar (South) resources in the US. The

implication is that a renewable-rich future is selected in this

study because of its economics. That is, existing and expected

future technology costs indicate that the most economically

attractive new energy investments are wind and solar; natural

gas is also part of the resource mix, providing operational

6

flexibility, and, depending on emission price, some energy.

2. Although it is possible, even likely, that transmission

expansion to the Electric Reliability Council of Texas

(ERCOT) may offer significant benefits to EI, WI, and ERCOT,

these potential benefits were not studied in this work in order to

limit its scope to that achievable within the time and with the

resources available.

3. All cross-seam transmission (existing and added) is HVDC.

Cross-seam AC transmission was not considered in our work

because it would synchronize what are now two asynchronous

grids, and therefore it may not offer the same flexibility of

choice in capacity and location as compared to use of HVDC.

A study to investigate the value of a hybrid HVDC/Ultra-High

Voltage AC solution could provide insight for a co-optimized

transmission and generation investment strategy.

4. The reduction in fuel cost is the dominant benefit for Design

3. However, this benefit alone is not sufficient to justify the

macrogrid overlay and underlying infrastructure. Savings in

regulation-up and down, contingency reserves, and

displacement of generation capacity required to meet future

PRM requirements also contribute.

5. Table 2 shows a comparison of performance between

Designs and 3. Although the differences in total generation

expansion between both designs are small, the difference in

creditable capacity, which is the sum of capacity available to

meet peak per (21), is significant. This is important because it

reflects the enhanced ability of Design 3 to benefit from load

diversity between regions during non-coincident peak load

times.

Table 2: Differences in generation and transmission investments between

Design 1 and Design 3, for the 50% renewable case

Metric (GW) Design 1 Design 3 Delta

Invested gen

(wind, solar, gas)

600

(386/172/36)

600

(392/169/38)

0

(7/-6/1)

Retired generation 240 294 54

2038 creditable capacity 838.5 794.1 -44.4

Invested AC transmission 228.9 195.1 -33.8

Invested DC transmission 0 125.8 125.8

To identify the effect of cross-seam transmission on the

location of generation investments, each interconnection was

divided into three subregions, as shown in Fig. 1. Figure 3

shows, for each subregion and for each interconnection, the

change in generation investments in Design 3 relative to those

of Design 1. This comparison indicates that cross-seam

transmission tends to move wind resources eastward and solar

resources westwards, linking load in each interconnection to the

most economically desirable resources. Finally, the breakdown

of generation technologies by fuel type for each interconnection

is included in Table 3. The 2038 infrastructure achieves a 50%

renewable generation (including hydro).

Figure 2: Difference in generation expansion between Design 1 and the

macrogrid design (Design 3) for the 50% renewable case

B. Robustness testing

The following sensitivities were performed to test the

robustness of each design to variation in a single assumption

relative to the base condition:

1) Low gas price: The gas price was changed from one that

began at 4.5 $/MBTU in 2024 and ended at 5.4 $/MBTU

in 2038 to one that began at 3.5$/MBTU and ended at

4.0$/MBTU. These prices are national averages; the

influence of spatial variation on gas prices was modeled.

2) Enforced RPS: State RPS constraints were enforced; but

the escalating emission price remained; this resulted in a

2038 renewable penetration level of 40%

3) No emission price: The emission price was zeroed; this

resulted in a 2038 renewable penetration level of 38%.

4) No sharing: Each RSG was required to satisfy its own

PRM.

Results of these sensitivities, together with those of the base

condition and the current policy condition, are shown in Fig. 4,

which portrays for each case both cross-seam investment

amount (bars) and B/C ratio (curve). We make the following

observations from this figure:

B/C ratio tracks cross-seam transmission capacity: The

conditions resulting in the highest cross-seam transmission

capacity are the conditions having the highest B/C ratio.

Base condition is best: The base condition (50% renewable

case) was chosen under two criteria: (a) it must be credible;

(b) it should maximize B/C ratio. These criteria were used

perceiving that studying other conditions would only be of

value if such a chosen base condition provides an

acceptable B/C ratio. It did, thereby justifying our

sensitivity studies.

All sensitivities invest > 10 GW cross-seam transmission:

This observation shows that even under low transmission-

friendly conditions, optimal investment plans still include

cross-seam transmission growth by a factor exceeding 7.

The no-sharing sensitivity has B/C < 1.0: This shows that

sharing planning and operating reserves is an important

value driver for cross-seam transmission. Because the

industry currently requires reserve requirements to be

satisfied intra-regionally, the benefits of reserve sharing

can be obtained only if the industry is willing to relax this

requirement.

The other four sensitivities have B/C > 1.0: This shows that

under conditions associated with a high-renewable future

greater than 40%, cross-seam transmission pays for itself,

based only on a 15-year period to assess savings generated

by generation investments and operational efficiencies.

(50)

(25)

-

25

50

Net-

Invest

ed

Gen

era

tio

n

Ca

pa

cit

y (

GW

) WIND SOLAR GAS

7

In addition, there are several benefits not included in our

B/C ratios, will likely cause them to be significantly higher

if they were included, as follows.

o Based on annual production costs observed in our study,

we estimate that operational savings beyond the 15-year

period to be between $1B and $4B/year;

o HVDC enables reliability improvements through

improved frequency response [12] and voltage control

[44];

o Increased interregional transmission enhances the ability

to minimize the cost of major disturbances resulting in

loss of regional generation capacity [45].

Figure 3: Incremental transmission between the EI and WI under

different future assumptions

VII. CONCLUSIONS

This paper provides an economic valuation of HVDC high

capacity transmission for the continental US, focusing

particularly on a macrogrid design. Under a high renewable

future, the design produces economic savings over a 15-year

period that exceed its investment cost; these savings result from

access to cost-effective renewables, decreased energy costs, and

less expensive flexibility services (regulating, contingency, and

planning reserves). Other potential benefits not quantified in

this study, that would require additional analysis, are obtained

at no cost; these benefits include enhanced reliability in terms

of frequency response and voltage control, and increased

resilience against large-scale disruptions and adaptability to

changes in policy and/or technology, both of the latter resulting

from increased interconnectedness. There are ongoing efforts to

address technological issues associated with design and

operation of high-capacity HVDC overlays, coordinated by the

IEEE Power Engineering Task Force on Ultra-Wide Area

HVDC Overlay Studies. Perhaps the greatest challenge lies in

the formation of appropriate policy to guide macrogrid

development, implementation, and operation. An initial effort

in this direction was taken via the Transgrid-X2030 Symposium

[46] from which resulted a steering committee to pursue follow-

on work. Much work remains in this arena; this paper

establishes that such effort is justified by the benefits a

macrogrid would deliver.

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IX. BIOGRAPHIES

Armando L. Figueroa-Acevedo (M’2008) received his BS and MS degrees in

electrical engineering from the Polytechnic University of Puerto Rico and the University of Puerto Rico, Mayagüez, in 2009, and ‘13, respectively, and Ph.D.

degree in wind, energy, science and policy and electrical engineering from Iowa State University in ’17.

Ali Jahanbani Ardakani received his BS and MS degrees in EE from

Amirkabir University in 2005 and 2008 and his Ph.D. degree from McGill University in 2014.

Hussam Nosair received his BS in EE and MS in Process Control from University of Alberta, Edmonton, AB in 2006 and 2009, respectively. He

received his PhD in EE from McGill University, Montreal, QC in 2016. He was

a postdoctoral research associate at Iowa State University, Ames, IA from 2016 to 2017, before joining the New York Independent System Operator, Rensselaer, NY as a research engineer.

Abhinav Venkatraman received his BE degree in Electrical and Electronics

Engineering from Anna U., India in 2014 and his MS degree in EE from Iowa State University in 2016. He is a Ph.D. student in EE at Iowa State University.

James McCalley (F’2003) received the BS, MS, and Ph.D. degrees from

Georgia Tech, all in EE. He is a Distinguished Professor and London Professor of Power Systems Engineering at Iowa State University. He was a transmission planning engineer at Pacific Gas & Electric 1985-1990.

Aaron Bloom received his BA in Political Science from Michigan State

University and his Masters in Public Administration from The Ohio State

University.

Dale Osborn (M’1964) received B. Sc. and M.S. degrees in electrical

engineering from the University of Nebraska, Lincoln, NE, USA. He was a Consulting Advisor in the Regulatory and Economic Studies group at the

Midcontinent Independent System Operator, Eagan, MN. He is now retired. Jay

Caspary received his BS in EE with an emphasis in Power Systems from the

University of Illinois – Urbana / Champaign in 1981. He is the Director –

Research, Development & Tariff Services at Southwest Power Pool in Little Rock, Arkansas with over 35 years of industry experience. He served as a Sr.

Policy Advisory for the Office of Electricity at the U.S. Department of Energy in support of grid modernization and research priorities in 2012-2013.

James Okullo received his BS in Electrical and Computer Engineering from

Seattle University, an MS in Electrical Engineering from the University of Washington, and an MS in Industrial Engineering from Purdue University. He is a Policy Studies Engineer at the Midcontinent ISO.

Jordan Bakke received his BS in electrical engineering from North Dakota

State University in 2009, and Masters of Business Administration from the

University of St. Thomas in 2014. He is the manager of Policy Studies at the Midcontinent ISO.

Harvey Scribner (SM’2003) received his BS in EE from Texas Tech University, Lubbock, Texas in 1987, MS in Management and Human Relations

from Abilene Christian University, Abilene, Texas in 1994. He is a Lead

Engineer in Research, Development & Tariff Services at the Southwest Power Pool in Little Rock, Arkansas with over 30 years of industry experience. He is a registered Professional Engineer in the State of Texas.